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Ten Things You Should Know about the Dynamic Conditional Correlation Representation

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Author Info

  • Massimiliano Caporin

    ()
    (Department of Economics and Management "Marco Fanno", University of Padova, Via del Santo 33, 35123 Padova, Italy)

  • Michael McAleer

    ()
    (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute, 3000 DR Rotterdam, Netherlands
    Department of Quantitative Economics, Complutense University of Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain
    Institute of Economic Research, Kyoto University, Kyoto 606-8501, Japan)

Abstract

The purpose of the paper is to discuss ten things potential users should know about the limits of the Dynamic Conditional Correlation (DCC) representation for estimating and forecasting time-varying conditional correlations. The reasons given for caution about the use of DCC include the following: DCC represents the dynamic conditional covariances of the standardized residuals, and hence does not yield dynamic conditional correlations; DCC is stated rather than derived; DCC has no moments; DCC does not have testable regularity conditions; DCC yields inconsistent two step estimators; DCC has no asymptotic properties; DCC is not a special case of Generalized Autoregressive Conditional Correlation (GARCC), which has testable regularity conditions and standard asymptotic properties; DCC is not dynamic empirically as the effect of news is typically extremely small; DCC cannot be distinguished empirically from diagonal Baba, Engle, Kraft and Kroner (BEKK) in small systems; and DCC may be a useful filter or a diagnostic check, but it is not a model.

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Bibliographic Info

Article provided by MDPI, Open Access Journal in its journal Econometrics.

Volume (Year): 1 (2013)
Issue (Month): 1 (June)
Pages: 115-126

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Handle: RePEc:gam:jecnmx:v:1:y:2013:i:1:p:115-126:d:26620

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Keywords: DCC representation; BEKK; GARCC; stated representation; derived model; conditional correlations; two step estimators; assumed asymptotic properties; filter;

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References

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  1. BAUWENS, Luc & LAURENT, Sébastien & ROMBOUTS, Jeroen, 2003. "Multivariate GARCH models: a survey," CORE Discussion Papers 2003031, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
  3. McAleer, Michael, 2005. "Automated Inference And Learning In Modeling Financial Volatility," Econometric Theory, Cambridge University Press, vol. 21(01), pages 232-261, February.
  4. McAleer, Michael & Chan, Felix & Hoti, Suhejla & Lieberman, Offer, 2008. "Generalized Autoregressive Conditional Correlation," Econometric Theory, Cambridge University Press, vol. 24(06), pages 1554-1583, December.
  5. Robert Engle & Neil Shephard & Kevin Shepphard, 2008. "Fitting vast dimensional time-varying covariance models," OFRC Working Papers Series 2008fe30, Oxford Financial Research Centre.
  6. Chang, Chia-Lin & McAleer, Michael & Tansuchat, Roengchai, 2011. "Crude oil hedging strategies using dynamic multivariate GARCH," Energy Economics, Elsevier, vol. 33(5), pages 912-923, September.
  7. Shawkat Hammoudeh & Tengdong Liu & Chia-Lin Chang & Michael McAleer, 2011. "Risk Spillovers in Oil-Related CDS, Stock and Credit Markets," Working Papers in Economics 11/17, University of Canterbury, Department of Economics and Finance.
  8. Lorenzo Cappiello & Robert F. Engle & Kevin Sheppard, 2006. "Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(4), pages 537-572.
  9. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(01), pages 122-150, February.
  10. Massimiliano Caporin & Michael McAleer, 2010. "Do We Really Need Both BEKK and DCC? A Tale of Two Multivariate GARCH Models," CIRJE F-Series CIRJE-F-713, CIRJE, Faculty of Economics, University of Tokyo.
  11. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-50, July.
  12. Matteo Manera & Alessandro Lanza & Michael McAleer, 2004. "Modelling Dynamic Conditional Correlations in WTI Oil Forward and Futures Returns," Working Papers 2004.72, Fondazione Eni Enrico Mattei.
  13. Massimiliano Caporin & Michael McAleer, 2008. "Scalar BEKK and indirect DCC," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(6), pages 537-549.
  14. Christian Hafner & Philip Hans Franses, 2009. "A Generalized Dynamic Conditional Correlation Model: Simulation and Application to Many Assets," Econometric Reviews, Taylor & Francis Journals, vol. 28(6), pages 612-631.
  15. Colacito, Riccardo & Engle, Robert F. & Ghysels, Eric, 2011. "A component model for dynamic correlations," Journal of Econometrics, Elsevier, vol. 164(1), pages 45-59, September.
  16. Maria Kasch & Massimiliano Caporin, 2008. "Volatility Threshold Dynamic Conditional Correlations: An International Analysis," "Marco Fanno" Working Papers 0065, Dipartimento di Scienze Economiche "Marco Fanno".
  17. Monica Billio & Massimiliano Caporin & Michele Gobbo, 2006. "Flexible Dynamic Conditional Correlation multivariate GARCH models for asset allocation," Applied Financial Economics Letters, Taylor and Francis Journals, vol. 2(2), pages 123-130, March.
  18. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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Citations

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Cited by:
  1. Michael McAleer, 2014. "Discussion of “Principal Volatility Component Analysis” by Yu-Pin Hu and Ruey Tsay," Working Papers in Economics 14/09, University of Canterbury, Department of Economics and Finance.
  2. Jean-David Fermanian & Hassan Malongo, 2014. "On the stationarity of Dynamic Conditional Correlation models," Papers 1405.6905, arXiv.org.
  3. Chang, C-L. & Hsu, H-K. & McAleer, M.J., 2014. "A Tourism Conditions Index," Econometric Institute Research Papers EI 2014-04, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  4. Christian M. Hafner & Michael McAleer, 2014. "A One Line Derivation of DCC: Application of a Vector Random Coefficient Moving Average Process," Working Papers in Economics 14/19, University of Canterbury, Department of Economics and Finance.
  5. Christian M. Hafner & and Michael McAleer, 2014. "A One Line Derivation of DCC: Application of a Vector Random Coefficient Moving Average Process," Tinbergen Institute Discussion Papers 14-087/III, Tinbergen Institute.
  6. Jean-David Fermanian & Hassan Malongo, 2013. "On the Stationarity of Dynamic Conditional Correlation Models," Working Papers 2013-26, Centre de Recherche en Economie et Statistique.

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